Method and apparatus for generation and employment of parcel productivity attributes for land parcel valuation

ABSTRACT

A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a productivity metric.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending U.S. patentapplications, each of which has a common assignee and common inventors,the entireties of which are herein incorporated by reference.

SERIAL NUMBER FILING DATE TITLE (CIBO.2001)      METHOD AND APPARATUSFOR GENERATION AND EMPLOYMENT OF AGRO-ECONOMIC METRICS FOR LAND PARCELVALUATION (CIBO.2003)      METHOD AND APPARATUS FOR GENERATION ANDEMPLOYMENT OF PARCEL PRODUCTION STABILITY ATTRIBUTES FOR LAND PARCELVALUATION (CIBO.2004)      METHOD AND APPARATUS FOR GENERATION ANDEMPLOYMENT OF PARCEL SUSTAINABILITY ATTRIBUTES FOR LAND PARCEL VALUATION(CIBO.2005)      METHOD AND APPARATUS FOR GENERATION OF LAND PARCELVALUATION BASED ON SUPPLEMENTED PARCEL PRODUCTIVITY ATTRIBUTES(CIBO.2006)      METHOD AND APPARATUS FOR GENERATION OF LAND PARCELVALUATION TAILORED FOR USE

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates in general to the field of agricultural landvaluation, and more specifically to methods and apparatus valuation ofagricultural parcels based upon inferred and estimated agriculturalfigures of merit supplemented by commercial sales prices of comparableparcels.

Description of the Related Art

The marketing and valuation of real property has consistently been atissue, though free market capitalism serves to easily resolve thevaluation problem for residential and commercial properties. As oneskilled in the art will appreciate, values for residential andcommercial properties are generally based upon recent sales and taxassessments for similar surrounding properties. In a general sense,houses are houses and buildings are buildings. Houses have bedrooms,bathrooms, kitchens, garages, porches, and other features. Likewise,buildings have offices, parking, and conference rooms. Some propertiesare newer, and some are older. As one skilled in the art willappreciate, because similar properties have a specific use—families livein houses and workers work in offices—the value of a given property isgenerally determined primarily by the cost per square foot ofsurrounding properties of similar quality.

Whereas the marketing of residential and commercial properties wasformerly exclusively performed by realtors who had exclusive access tomultiple-listing services, since the advent of online services such asZillow, Redfin, Reonomy, and LoopNet, user-friendly portals have beenprovided that enable buyers to enter search parameters for propertiesand access reports for selected properties that provide estimated valuesfor the properties along with a significant amount of other helpfulinformation. This information may include pictures, drawings,descriptions, and reviews of the properties, and may further includefeatures of the properties (e.g., number of bedrooms and bathrooms),school district information, shopping that is close by, publictransportation access, etc.

This application considers a different type of property, namely landthat is used for agricultural purposes. In a broad sense, anagricultural parcel is land that worked in order to make a profit, andthe type of work performed on a parcel may be the growing of crops(e.g., corn, soy, wheat, timber), raising of livestock (e.g., chickens,cattle, pigs, fish), or the extraction of products from livestock (e.g.,dairies). As such, it is not as easy to estimate the value of aparticular agricultural parcel using comparable sales prices alone, forthe value of the particular parcel is highly correlated with the uniqueproperties of that parcel to support the parcel's ability to beprofitable. As one skilled in the art will appreciate, the profitabilityof agricultural parcels is a function of weather conditions, locationand topography, how the productive activities have been previouslymanaged, and how the productive activities are currently managed. As oneskilled in the art will also appreciate, there is plethora ofinformation of this sort that is both publicly and commerciallyavailable for particular parcels, but outside of the USDA Census data,the information differs from county to county and state to state. Somecounties may have more data related to yield, while other counties willhave more data related to management practices. The amount of dataavailable is massive, but the present inventors have noted that there isno extant mechanism for comparing parcels that are in differentcounties/states because there is no common basis for making comparisons.

Though there are present-day platforms that provide for marketing ofagricultural parcels, the present inventors note that such platformsrely solely on data that is common amongst all properties in a region.That is, the properties are marketed based upon their total and tillableacreage, primary/secondary crops, historical yields, comparable sales,and attributes that are common to all agricultural properties. However,the present inventors have noted a need in this field of technology toprovide techniques and mechanisms that not only utilize all of theattributes that are common to these properties, but that also leveragethe vast amount of science that is available to predict crop growthunder forecasted weather and soil conditions, and that in addition areable to utilize predicted crop growth in conjunction with the massiveamount of data that is not common for parcels in differingcounties/states to generate objective metrics that enable anapples-to-apples comparison of a given parcel's ability to supportproduction profitability in view of all of the parcels within aprescribed region, be that region county level, state level, or growingregion (e.g., the Corn Belt).

Accordingly, what is needed is a method and apparatus for translatingthe vast amount of public data, commercial data, scientific data, andpredicted crop growth data into agro-economic metrics that enable a userto make objective comparisons between similar agricultural parcels.

In addition, what is needed is a mechanism for employing these metricsas supplemented by commercial sales data in order to provideagricultural valuations for these parcels that expresses in dollars peracre the value of the properties in terms of their ability to supportproductive efforts.

What is further needed is a method and system that enables a user todetermine which productive attributes of agricultural parcels are moreimportant than others based upon the user's role (e.g., farmer,enterprise farming corporation, lending institution, insurer, etc.)

SUMMARY OF THE INVENTION

The present invention, among other applications, is directed to solvingthe above-noted problems and addresses other problems, disadvantages,and limitations of the prior art by providing a superior technique foremploying a combination of public data, commercial data, field trialdata, and crop simulation data to generate agro-economic metrics andobjective valuations for a vast number of agricultural parcels. In oneembodiment, a method for agricultural land parcel valuation is provided,the method comprising: accessing data corresponding to each of aplurality of parcels within a prescribed region, the data comprisingcorresponding management practices, corresponding historical weatherconditions, corresponding locations and topography, corresponding remotesense images, corresponding soil types, and corresponding crop types;assessing and ranking the corresponding management practices for theeach of the plurality of parcels; generating simulation inputs for theeach of the plurality of parcels, where the simulation inputs comprisehighest ranked corresponding management practices, the correspondinghistorical weather conditions, the corresponding locations andtopography, the corresponding soil types, and the corresponding croptypes; simulating crop growth for the each of the plurality of parcelsover a prescribed number of previous years, where the simulating employsthe simulation inputs provided by the generating; and employing selectedoutputs from said simulating to calculate agricultural metrics and avaluation corresponding to the each of the plurality of parcels, whereinthe agricultural metrics and the valuation for the each of the pluralityof parcels are expressed relative to all of the plurality of parcelswithin the prescribed region, the agricultural metrics comprising aproductivity metric that is calculated as a function of a weightedaverage of yearly primary crop yield simulation outputs for the each ofthe plurality of parcels, wherein weights for the weighted averagecomprise fractions of tillable acreage for each of a plurality of soiltype zones within the each of the plurality of parcels.

One aspect of the present invention contemplates a computer-readablestorage medium storing program instructions that, when executed by acomputer, cause the computer to perform a method for agricultural landparcel valuation, the method comprising: accessing data corresponding toeach of a plurality of parcels within a prescribed region, the datacomprising corresponding management practices, corresponding historicalweather conditions, corresponding locations and topography,corresponding remote sense images, corresponding soil types, andcorresponding crop types; assessing and ranking the correspondingmanagement practices for the each of the plurality of parcels;generating simulation inputs for the each of the plurality of parcels,where the simulation inputs comprise highest ranked correspondingmanagement practices, the corresponding historical weather conditions,the corresponding locations and topography, the corresponding soiltypes, and the corresponding crop types; simulating crop growth for theeach of the plurality of parcels over a prescribed number of previousyears, where the simulating employs the simulation inputs provided bythe generating; and employing selected outputs from said simulating tocalculate agricultural metrics and a valuation corresponding to the eachof the plurality of parcels, wherein the agricultural metrics and thevaluation for the each of the plurality of parcels are expressedrelative to all of the plurality of parcels within the prescribedregion, the agricultural metrics comprising a productivity metric thatis calculated as a function of a weighted average of yearly primary cropyield simulation outputs for the each of the plurality of parcels,wherein weights for the weighted average comprise fractions of tillableacreage for each of a plurality of soil type zones within the each ofthe plurality of parcels.

Another aspect of the present invention envisages a system foragricultural land parcel valuation, the system comprising: anagricultural valuation server, configured to access data correspondingto each of a plurality of parcels within a prescribed region, the datacomprising corresponding management practices, corresponding historicalweather conditions, corresponding locations and topography,corresponding remote sense images, corresponding soil types, andcorresponding crop types, the agricultural valuation server comprising:a management practices processor, configured to assess and rank thecorresponding management practices for the each of the plurality ofparcels, and configured to generate simulation inputs for the each ofthe plurality of parcels, where the simulation inputs comprise highestranked corresponding management practices, the corresponding historicalweather conditions, the corresponding locations and topography, thecorresponding soil types, and the corresponding crop types; a cropsimulation processor, coupled to the management practices processor,configured employ the simulation inputs to simulate crop growth for theeach of the plurality of parcels over a prescribed number of previousyears; an agricultural metrics processor, configured to employ selectedoutputs from said simulating to calculate agricultural metricscorresponding to said each of said plurality of parcels, wherein saidagricultural metrics are expressed relative to all of said plurality ofparcels within said prescribed region, said agricultural metricscomprising a productivity metric that is calculated as a function of aweighted average of yearly primary crop yield simulation outputs for theeach of the plurality of parcels, wherein weights for the weightedaverage comprise fractions of tillable acreage for each of a pluralityof soil type zones within the each of the plurality of parcels; and avaluation processor, configured to employ selected outputs from thesimulating to calculate a valuation corresponding to the each of theplurality of parcels, where the valuation is expressed relative to allof the plurality of parcels within the prescribed region.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the presentinvention will become better understood with regard to the followingdescription and accompanying drawings where:

FIG. 1 is a block diagram illustrating an agricultural parcel valuationand search system according to the present invention;

FIG. 2 is a block diagram depicting an exemplary schema for a parceldatabase according to the present invention;

FIG. 3 is a block diagram featuring system level flow for valuation andgeneration of metrics associated with agricultural parcels within aprescribed growing region;

FIG. 4 is a flow diagram showing processing of remote sensed data fordetermination of stability zones within a parcel;

FIG. 5 is a flow diagram illustrating processing and ranking ofagricultural management practices for generation of inputs to a cropsimulation processor according to the present invention;

FIG. 6 is a flow diagram detailing a method for translation of cropsimulation outputs into a productivity metric for an agricultural parcelthat ranks the parcel relative to all other parcels within a specifiedgrowing region;

FIG. 7 is a flow diagram detailing a method for translation of remotesensed data into a stability metric for an agricultural parcel accordingto the present invention;

FIG. 8 is a flow diagram detailing a method for translation of cropsimulation outputs into a sustainability metric for an agriculturalparcel that ranks the parcel relative to all other parcels within aspecified growing region;

FIG. 9 is a flow diagram detailing a method for translation ofagricultural metrics and comparable sales prices into a valuation for anagricultural parcel relative to all other parcels within a specifiedgrowing region.

FIG. 10 is a block diagram illustrating an agricultural valuation serveraccording to the present invention;

FIG. 11 is a block diagram depicting a client device according to thepresent invention;

FIG. 12 is a diagram featuring an exemplary advanced search displayaccording to the present invention such as might be presented by theclient device of FIG. 11;

FIG. 13 is a diagram showing an exemplary advanced search interactivedisplay according to the present invention such as might be presented bythe client device of FIG. 11;

FIG. 14 is a diagram illustrating an exemplary advanced search resultsdisplay according to the present invention such as might be presented bythe client device of FIG. 11;

FIG. 15 is a diagram detailing an exemplary parcel report with valuationdetails display according to the present invention such as might bepresented by the client device of FIG. 11;

FIG. 16 is a diagram detailing an exemplary parcel report withproductivity metric details display according to the present inventionsuch as might be presented by the client device of FIG. 11;

FIG. 17 is a diagram detailing an exemplary parcel report with stabilitymetric details display according to the present invention such as mightbe presented by the client device of FIG. 11; and

FIG. 18 is a diagram detailing an exemplary parcel report withsustainability metric details display according to the present inventionsuch as might be presented by the client device of FIG. 11.

DETAILED DESCRIPTION

Exemplary and illustrative embodiments of the invention are describedbelow. It should be understood at the outset that, although exemplaryembodiments are illustrated in the figures and described below, theprinciples of the present disclosure may be implemented using any numberof techniques, whether currently known or not. In the interest ofclarity, not all features of an actual implementation are described inthis specification, for those skilled in the art will appreciate that inthe development of any such actual embodiment, numerousimplementation-specific decisions are made to achieve specific goals,such as compliance with system-related and business-related constraints,which vary from one implementation to another. Furthermore, it will beappreciated that such a development effort might be complex andtime-consuming, but would nevertheless be a routine undertaking forthose of ordinary skill in the art having the benefit of thisdisclosure. Various modifications to the preferred embodiment will beapparent to those skilled in the art, and the general principles definedherein may be applied to other embodiments. Therefore, the presentinvention is not intended to be limited to the particular embodimentsshown and described herein but is to be accorded the widest scopeconsistent with the principles and novel features herein disclosed.

The present invention will now be described with reference to theattached figures. Various structures, systems, and devices areschematically depicted in the drawings for purposes of explanation onlyand so as to not obscure the present invention with details that arewell known to those skilled in the art. Nevertheless, the attacheddrawings are included to describe and explain illustrative examples ofthe present invention. Unless otherwise specifically noted, articlesdepicted in the drawings are not necessarily drawn to scale.

The words and phrases used herein should be understood and interpretedto have a meaning consistent with the understanding of those words andphrases by those skilled in the relevant art. No special definition of aterm or phrase (i.e., a definition that is different from the ordinaryand customary meaning as understood by those skilled in the art) isintended to be implied by consistent usage of the term or phrase herein.To the extent that a term or phrase is intended to have a specialmeaning (i.e., a meaning other than that understood by skilled artisans)such a special definition will be expressly set forth in thespecification in a definitional manner that directly and unequivocallyprovides the special definition for the term or phrase. As used in thisdisclosure, “each” refers to each member of a set, each member of asubset, each member of a group, each member of a portion, each member ofa part, etc.

Applicants note that unless the words “means for” or “step for” areexplicitly used in a particular claim, it is not intended that any ofthe appended claims or claim elements are recited in such a manner as toinvoke 35 U.S.C. § 112(f).

Definitions

Central Processing Unit (CPU): The electronic circuits (i.e.,“hardware”) that execute the instructions of a computer program (alsoknown as a “computer application,” “application,” “application program,”“app,” “computer code,” “code process,” “code segment,” or “program”) byperforming operations on data that may include arithmetic operations,logical operations, and input/output operations. A CPU may also bereferred to as a processor

In view of the above background discussion on how agricultural landparcels are presently valued, a discussion of the present invention willbe provided with reference to FIGS. 1-18. The present inventionovercomes the problems associated with present-day techniques byproviding methods and apparatus that translate commercial and publicdata, remotely sensed data, topography data, farm management practices,field trials data, and crop simulation data into agriculturallymeaningful metrics and corresponding objective agricultural valuationsfor parcels relative to all other parcels within a growing region thatenable different classes of users, as a function of user role, to makedecisions on selected parcels more accurately than has heretofore beenprovided for by present-day valuation techniques.

Referring to FIG. 1, a block diagram is presented illustrating anagricultural parcel valuation and search system 100 according to thepresent invention. The system 100 may include an agricultural valuationserver 130 that is coupled to one or more client devices 101-103 throughthe internet cloud 110. The client devices 101-103 may include one ormore desktop/laptop computers 101 that execute desktop/laptop clientapplications 104 for communication and interaction with the valuationserver 130 through the internet cloud 110. The client devices 101-103may also include one or more smart tablet computers 102 that executetablet client applications 105 for communication and interaction withthe valuation server 130 through the internet cloud 110. The clientdevices 101-103 may further include one or more smartphone devices 103that execute smartphone client applications 106 for communication andinteraction with the valuation server 130 through the internet cloud110.

The valuation server 130 is coupled to a truth database 121, a publicdatabase 122, a commercial database 123, and a scientific database 124.Though represented in the block diagram as single databases 121-124,each of the databases 121-124 may comprise a substantial number ofdatabases through which the valuation server 130 may access truth-baseddata, public data, commercial data, and scientific data in order totranslate this data into agriculturally meaningful metrics andvaluations for a vast number of agricultural parcels.

Preferably, truth-based data includes field trial results that are themeasurements taken by farming partners who plant and harvest crops undera wide range of specified scenarios. These field trial results areemployed by the valuation server to test and improve the accuracy ofcrop simulations that may be performed to generated metrics andvaluations for similar parcels, and that are employed to scalesimulations to entire growing regions.

Public data comprises a wide variety of sources such as, but not limitedto, county records, United States Department of Agriculture monthlyreports; parcel geographic coordinates data and topography; soil typesand layering (e.g., Soil Survey Geographic Database (SSURGO); historicalcrop planting harvesting, and yield data; soil type indexes (e.g., CornStability Rating 2 (CSR2); historical and historical and forecastedweather data; and satellite and aerial image data taken acrossagriculturally meaningful spectral band (e.g., LANDSAT, SENTINEL) thatmay be processed by the valuation server 130 to understand crop types,rotations, key management practices (e.g., planting dates, tillagedates, fertilization dates, harvesting dates), and stages of growth atany given time.

Commercial data may comprise any of the public data that is aggregatedor formatted for ease of access by the valuation server 130.

Scientific data may comprise selected results of global scientificresults taken from published literature. The results are provided to thevaluation server 130 to validate crop simulations and to ensure that thesimulations are accurate across a wide range of management scenarios andweather conditions. In one embodiment, crop simulation results arecompared to scientific research data obtained under similar managementpractices and weather conditions.

The agricultural valuation server 130 may include a presentationprocessor 141 that is coupled to a parcel database 151. The presentationprocessor 141 comprises a user interface (UX) component 142, a searchengine component 143, and a user database 144.

The valuation server 130 may further comprise an agricultural metricsprocessor 152, a crop simulation processor 153, and a valuationprocessor 154, all of which are coupled to the parcel database 151. Thevaluation server 130 may further comprise a remote sense processor 156that is coupled to a management practices processor 155 and to theagricultural metrics processor 152. The management practices processor155 is coupled to the crop simulation processor 153.

In operation, records corresponding to agricultural parcels in a regionare created, iterated, and revised as a function of newly available datafrom one or more of the databases 121-124 and applicable results fromrecent crop simulations performed by the crop simulation processor 153.The records are stored in the parcel database 151 for access by theagricultural metrics processor 152, the valuation processor 154, and thepresentation processor 141. Users may execute the client applications104-106 on the client devices 101-103 to specify constraints, weights,and search parameters for one or more parcel records stored within theparcel database 151. The user interface processor 142 executes in orderto transmit display windows to the client devices 101-103 via theirrespective client applications 104-106 to enable the users to specifythe constraints, weights, and search parameters. The client applications104-106 may transmit the constraints, weights, and search parameters tothe presentation processor 141 through the internet cloud 141. In oneembodiment, the constraints, weights, and search parameters are storedin corresponding user records within the user database 144 to acceleratesubsequent searches. Upon receipt of the constraints, weights, andsearch parameters, the search engine processor 143 employs thecorresponding user records to access one or more records within theparcel database 151 that satisfy the constraints, weights, and searchparameters. The one or more records within the parcel database 151 thatsatisfy the constraints, weights, and search parameters may also bestored in corresponding user records within the user database 144 toaccelerate subsequent searches, and the one or more records within theparcel database 151 that satisfy the constraints, weights, and searchparameters are provided by the search engine processor 143 to the userinterface processor 142, which formats the one or more records fordisplay by the client applications 104-106 on the client devices 101-103according to device type, and the presentation processor transmits theone or more records to the client devices 101-103 along with contextualmetadata corresponding to the one or more parcels (e.g., parcels shownon a map) that enable the users to visualize and better comprehendresults of their searches.

In one embodiment, users may iteratively refine searches by specifyingadditional constraints, weights, and search parameters to further targetsearch results that are of interest, and these results are additionallystored in the corresponding user records within the user database 144.

Upon selection of a specific parcel record, the presentation processor141 may transmit fields within the records that are formatted by theuser interface processor 142 for display to the user along with metadatathat enable the user to visualize and comprehend the record fieldsassociated with the parcel, thus providing the user with a substantiallyimproved method for making an informed decision regarding acorresponding agricultural parcel based upon user category (e.g., smallfarmer, enterprise farming corporation, underwriter, lender, insurer,etc.).

The remote sense processor 156 may process satellite/aerial images, andmay merge selected images to determine vegetative indices, to estimatemissing image data, and to determine parcel crop stability zones. Themanagement practices processor 155 may access data from the databases121-124 corresponding to management practices associated with parcelsand rank the outputs against other management practice data that isreceived from one or more of the databases 121-124. In turn, themanagement practices processor 155 may augment sparse or incomplete datain order to provide location-specific inferences for a number of keycrop management practices including, but not limited to planted croptype, specific cultivar or crop variety, planting data, planting density(i.e., seeds per acre and row spacing), fertilizer application (e.g.,dates and amounts), and irrigation practices. In one embodiment, highestranked management practices are employed to construct simulation inputsto the crop simulation processor 153 for modeling of required multi-yearcrop simulations. For example, management practices from the truthdatabase 121 may be ranked higher than crop simulation results. In theabsence of truth data for a parcel, state guidelines or managementpractices rules of thumb may be employed to build directives forsimulations.

The results of the crop simulations are employed by the agriculturalmetrics processor 152 and the valuation processor 154 to iterativelytranslate simulation results and data provided by the databases 121-124into figures of merit and an agricultural valuation for every parcelwithin the parcel database 151. In one embodiment, the number of parcelrecords in the parcel database 151 comprises in excess of 20 millionparcels located in the United States.

In one embodiment, the crop simulation processor 153 preferablycomprises a mechanistic crop model such as the Systems Approach to LandUse Suitability (SALUS), the initial version of which was developed atMichigan State University and which has been the subject of 20 years oftesting across hundreds of fields in 46 countries, more than 25 PhDdissertations, over 200 peer-reviewed journal articles, and thousands ofacademic citations. It is not the purpose of the present application toprovide an in depth tutorial on mechanistic crop modeling, but rather todisclose how results of crop simulations performed by crop simulationprocessor 153 are translated into agricultural metrics and acorresponding agricultural valuation that enables a user to makeinformed and meaningful decisions for one or more parcels. For atutorial on SALUS, the reader is directed to the publications below:

-   Basso, B., & Ritchie, J. T. (2015). Simulating crop growth and    biogeochemical fluxes in response to land management using the SALUS    model. In S. K. Hamilton, J. E. Doll, & G. P. Robertson (Eds.), The    ecology of agricultural landscapes: long-term research on the path    to sustainability. New York, N. Y., USA: Oxford University Press,    252-274;-   Basso, B., Ritchie, J. T., Grace, P. R., Sartori, L. (2006).    Simulation of tillage systems impact on soil biophysical properties    using the SALUS model. Italian Journal of Agronomy, 1(4), 677. doi:    10.4081/ija.2006.677;-   Albarenque, S. M., Basso, B., Caviglia, O. P., Melchiori, R. J.    (2016). Spatio-temporal nitrogen fertilizer response in maize: field    study and modeling approach. Agronomy Journal, 108(5), 2110. doi:    10.2134/agronj2016.02.0081; and-   Partridge, T. F., Winter, J. M., Liu, L., Kendall, A. D., Basso, B.,    Hydnman, D. (2019). Mid-20th century warming hole boosts U.S. maize    yields. Environmental Research Letters. doi:    10.1088/1748-9326/ab422b

The present inventors note that the crop simulation processor 153according to the present invention is continuously improved as afunction of new scientific and truth data in order to expand todifferent crop types and to provide scaling to address practical needsof agriculture from subfield to continental scales. The aforementionedcrop model is a subset of a larger simulation engine within the cropsimulation processor 153 which uses a combination of farmer reporteddata, government and academic statistics, and remote sensing to build adetailed scenario that describes genotypic conditions (i.e., cropparameters representing genotypic potentials of a crop), environmentalconditions (i.e., weather, physical soil properties, and chemical soilproperties), and management conditions (e.g., planting dates, fertilizerapplication dates and amounts, tillage date, depth, and material, etc.)of a growing crop. Based on these input conditions, the crop modelcalculates plant growth stage, plant leaf area, solar energy absorbedthrough the leaves, biomass accumulated in different plant tissues, andwater and nutrient uptake by the roots, and saves outputs for that day.These variables are calculated at every time step until the cropmatures.

Accordingly, the crop simulation processor 153 is configured to describedevelopment and growth of a crop within an agricultural parcel all theway from planting to maturity. Plant development describes the timing ofevents that occur during the plant life cycle that induce changes ingrowth rates and partitioning of dry matter to different plant tissues.The simulation processor 153 calculates state variables representingvarious aspects of development and growth at each daily time step andfurthermore describes the different components of the soil layers andhow these interact with the environment. Thus, the crop simulationprocessor 153 may estimate the amount of water and nutrients availablefor uptake by a growing plant. Root development occurs at each dailytime step: root tips progress through the soil layers, and root massincreases. This results in water and nutrient uptake in soil horizonsthat are in contact with the plant's rooting system, proportional to theroot mass distribution in each soil horizon. Advantageously, the cropsimulations performed by the crop simulation processor 153 reflect thecomplex interactions whereby soil and weather influence plant growth andhow plant growth subsequently changes the soil dynamic. Outputs of thecrop simulation processor 153 include, but are not limited to, yields,nitrogen stress, drought stress, biomass accumulation, growth stages,nitrogen uptake, nitrogen use efficiency, and water use efficiency.

In one embodiment, constraints, weights, and search parameters that auser may specify to access parcel records in the parcel database 151include, but are not limited to, growing region, state, county, zipcode, Public Land Survey System (PLSS), keywords, parcel owner name,historical land use (e.g., crop type), land type (e.g., farm, dairy,ranch, forest, etc.), parcel acreage, tillable area, and agriculturalmetrics and valuations generated by the valuation server 130.

As will be described in further detail below, the agricultural metricsand agricultural valuations generated by the agricultural valuationserver 130 enable a user functioning in a specific role (e.g., farmer,enterprise, underwriter, etc.) to understand the value of a particularparcel from the user's perspective, which is a significant improvementover present-day techniques that merely employ sales of comparableparcels. Depending on the user's role, agricultural metrics may beexpressed as productivity of a parcel, production risk of the parcel,and the parcel's ability to be sustainably managed. For a user purelyinterested in farming, a parcel's productivity and stability (i.e.,productivity risk) are paramount. However, for an enterprise that isfocused on reducing carbon emissions, a different metric (e.g.,sustainability) may take precedence. In one embodiment, the agriculturalvaluation assigned to a parcel employs one or more of the agriculturalmetrics as a function of the user's role as supplemented by comparablesales to express an agricultural value in dollars as opposed to just avalue that is based on comparable parcels. Advantageously, the user isexposed to a valuation of a parcels based upon the parcel's agriculturalpotential, which is a substantial improvement over that which hasheretofore been provided.

The valuation server 130 according to the present invention may compriseone or more application programs executing thereon to perform theoperations and functions described above, and which will be disclosed infurther detail with reference to FIG. 10.

Turning now to FIG. 2, a block diagram is presented depicting anexemplary schema for a parcel database 200 according to the presentinvention, such as the parcel database 151 of FIG. 1. The schema 200 mayinclude a geographic feature table 201 that is linked to a plurality offeature detail tables 204 in a one-to-many architecture. The geographicfeature table 201 may include a plurality of records 202 having aplurality of data fields 203. Each of the feature detail tables 204 mayinclude a plurality of records 205 having a plurality of data fields206. The plurality of data fields 203 in each of the geographic featurerecords 202 include a geographic feature ID field (GFID), which is theprimary key for the geographic feature table 201 and which is unique foreach of the plurality of records 202. The plurality of data fields 203in each of the geographic feature records 202 additionally include ageographic feature type field (GFTYPE), a boundary field (BOUNDARY), acentroid field (CENTROID), and an area field (AREA). GFTYPE specifiesone of a plurality of geographic feature types that include, but are notlimited to, parcel, county, state, and growing region. BOUNDARY includesgeographic coordinates (e.g., longitude and latitude) that describe ageographic boundary for the land area corresponding to the GFID.CENTROID includes a geographic centroid coordinates for the land areacorresponding to the GFID. And AREA includes the area (e.g., acres,square meters, etc.) of the land area corresponding to the GFID.

The plurality of data fields 206 in each of the feature detail records205 include a feature detail ID field (FDID), which is the primary keyfor a corresponding feature detail table 204 and which is unique foreach of the plurality of records 205. Each of the feature detail records205 may also include a geographic feature type ID field (GFID), which isthe foreign key that links a feature detail record 205 back to acorresponding geographic feature record 202 in the geographic featuretable 201. Each of the feature detail records 205 may further include afeature detail type field (FDTYPE) and a feature detail data field(FDDATA). FDTYPE specifies one of a plurality of feature detail typesthat include, but are not limited to, results generated by the cropsimulation processor 153 for the corresponding FDID and GFIDcombination, a particular agricultural metric (e.g., productivity score,stability score, sustainability score) generated by the agriculturalmetrics processor 152 for the corresponding FDID and GFID combination,an agricultural valuation generated by the valuation processor 154 forthe corresponding FDID and GFID combination, stability zone boundariesgenerated by the remote sense processor 156 for the corresponding FDIDand GFID combination, and descriptive metadata taken from the databases121-124 for the corresponding FDID and GFID combination.

Accordingly, a given geographic feature (e.g., a 40-acre farm in MilfordCounty, Iowa) may be described in the parcel database 151 in terms ofits geographic boundary, centroid, and area within a record 202 in thegeographic feature table 201, and this record 202 may be linked to anumber of feature detail records 205 in different feature detail tables204 that contain feature detail data for a corresponding number offeature detail types.

As one skilled in the art will appreciate, deciphering the history andpotential of a parcel is critical to understanding the parcel's ultimateeconomic value; however, this type of information is typically notavailable outside of hard-to-come-by operator data, and without accessto this data, those interested in valuing a parcel are typically limitedto public soil maps and state-level productivity rankings, which areinadequate for representing actual field conditions. In addition, oneskilled in the art will appreciate that most states don't have aconsistent productivity score that allows for comparison of parcelsacross states, and that state productivity scores are based onhistorical information of weather and soil. In contrast, the metrics andvaluations provided for by the present invention are 1) consistent and2) based upon topography, soil, and crop simulations that are validatedby remote sensing in test fields.

Referring now to FIG. 3, a flow diagram is presented featuring systemlevel flow for valuation and generation of metrics associated withagricultural parcels within a prescribed growing region, such as mightbe performed by the agricultural valuation server 130 of FIG. 1, andsuch as might be stored in the exemplary parcel database records 200 ofFIG. 1. Flow begins at block 304 where databases 302 are accessed anddata therefrom is cleansed of error and formatted for analysis andsimulation. In one embodiment, the databases 302 comprise the databases121-124 of FIG. 1. As one skilled in the art will appreciate, one of themost challenging issues associated with the processing of so-called “bigdata” is handling lots of data from different sources that is formatteddifferently, updated differently, and that contains different types oferrors. Accordingly, the accessing, cleansing, and formatting data inblock 304 may comprise a core set of steps for each data source, namelydownloading the data, assessing and cleansing the data, and formattingand storing the data.

The downloading step may comprise automating downloads for those datasources that require more frequent updates. For example, irrigation datamay only have to be downloaded once a year, but weather data has to bedownloaded daily. In addition, for data that is retroactively updateddue to more accurate sources, the automation task may comprisedownloading more recent days in the past, comparing their data tocurrent downloads of those days, and continuing to update the data untilit stabilizes.

The downloading step may additionally comprise automating ingestion ofdifferent data formats such as, but not limited to, CSV or Excel files,public database formats, scanned images, Power Point files, and PDFfiles.

Once download, the data is assessed and cleansed, which may compriseremoval of duplicate information, inferring missing values, substitutingfor unconventional characters and symbols, and removal of outlier values(e.g., tax assessment data having extra zeros). For data that will beingested regularly, the present invention contemplates automation ofassessment and cleansing. Inference of missing values may compriseemploying alternative data (e.g., using state/national averagemanagement practices in situations where county records lack such data).It is noted that assessment and cleansing of satellite/aerial data isparticularly challenging since clouds or other obstacles may obscureimages. In one embodiment, the assessing and cleansing step comprisesautomated processes to filter out images with obscured data.

The formatting and storage of the data may include determination ofwhether data may be stored in a single file or may require a database orfile store (e.g., Amazon Simple Storage Service). Preferably smallamounts of data are stored as files, such as county-level USDA data) andlarge amounts of data (e.g., weather data, satellite/aerial imagery,field trials data, and parcel metadata) are stored in databases whichare accessed via a microservice architecture. One example of such is aservice that accepts parcel boundaries to access soil zones for a givenparcel. Another example is a service that accepts parcel boundaries toaccess the yearly weather for the given parcel.

At block 306, data that has been cleansed and formatted in block 304 isanalyzed for each parcel to generate inferences regarding a number ofattributes that include, but are not limited to, crop types, rotations,key management practices (e.g., planting dates, tillage dates,fertilization dates, harvesting dates), and stages of crop growth at anygiven time. These inferences may be generated by the managementpractices processor 155 of FIG. 1, and are provided to the dataselection block 308.

At the data selection block 308, the generated parcel attributes alongwith other data necessary for simulation of crops corresponding to allof the parcels are selected on a parcel by parcel basis. A subset ofdata selection may comprise building a list of inputs to a cropsimulation model within the crop simulation processor 153 of FIG. 1. Theprocess of building simulation inputs is shown in block 309 and may alsocomprise a set of directives that guide building of the simulationinputs according to best practices for parcels within a given area.Preferably, the simulation inputs are built according to automateddirectives that rely solely on inferred data, without humanintervention. In one embodiment, data selection provides for thecombination of known and inferred data that includes managementpractices, soil data, and weather data (including long-term forecasts)according to the set of directives. From these directives, the dataselection block 308 produces a complete input set for crop simulation.Flow then proceeds to block 310.

At block 310, the simulation inputs for all parcels within a prescribedregion are provided to the crop simulation processor 153, which executescrop simulations for respective crops corresponding to each of theparcels over a period of years, where the crops and number of years areprovided by the simulation inputs. In one embodiment, the prescribedregion may be the entire United States. In another embodiment, theprescribed region may be a specific growing region (e.g., the Corn Belt,the Wheat Belt). In a further embodiment, the prescribed region maycomprise a given state (e.g., Iowa). In yet another embodiment, theprescribed region may comprise a county (e.g., Marshall County, Iowa).Though the prescribed regions are preferably associated within parcelswithin the United States, the present inventors note that the system 100according to the present invention may be adapted for practice withinany country in the world. Thus, according to the inputs provided byblock 308, crop simulations are run at scale by the crop simulationprocessor 153 to generate parcel yields per planting season along with anumber of other corresponding simulation outputs such as, but notlimited to, plant growth stage, plant leaf area, solar energy absorbedthrough the leaves, biomass accumulated in different plant tissues, andwater and nutrient uptake by the roots. In one embodiment, crop growthis simulated daily and outputs for each day are saved and employed asparameters for the next growing day. These variables are calculated atevery time step until a crop for each parcel matures. In one embodiment,the crop simulation processor 153 is configured to perform millions ofcrop simulations in hours and is configured to run simulations for corn,soybeans, cotton, or wheat on any agricultural location in the U.S. Thesimulation results are stored in the parcel database 318. Flow thenproceeds to block 314.

At block 314, the agro-economic metrics processor 152 accesses thedatabase 318 and employs the simulation results to generate a pluralityof agro-economic metrics for every parcel in the parcel database 318.Some of the plurality of metrics, as will be described in further detailbelow, may exclusively employ simulation outputs while others of themetrics may employ simulation outputs in combination with data retrievedfrom the databases 302. Some of the metrics may not require simulationoutputs, but rather may utilize satellite/aerial image data. Some of themetrics may be iteratively generated as is shown in the diagram 300,while other metrics may be directly generated. In one embodiment thefollowing agro-economic metrics are generated at block 314:

-   -   a productivity score for each parcel that is expressed as a        value from 0 to 100 relative to all other parcels within a        prescribed region;    -   a stability (or, “field reliability”) score for each parcel that        is expressed as a value from 0 to 100 relative to all other        parcels within a prescribed region; and    -   a sustainability score for each parcel that is expressed as a        value from 0 to 100 relative to all other parcels within a        prescribed region.

The productivity score is a measure of the ability of a particularparcel to support crop production under common management practices inits historical environment. In this context, productivity is the outcomeof interactions among the main components of a crop production systemthat impact crop performance, namely weather, soil, and managementpractices. The stability score is a measure of the risk associated withproducing crops on the particular parcel of land over a range of years.The sustainability score is a measure of a parcel's ability tosustainably grow crops over a range of years. Flow then proceeds toblock 316.

At block 316, the valuation processor 154 accesses the metrics generatedat block 314 along with data from the databases 302 and calculates anagricultural value for each of the parcels that considers the relativescoring of the agricultural metrics as supplemented by comparable salesof surrounding parcels. In one embodiment, as will be described infurther detail below, the productivity score for each parcel is employedin conjunction with productivity scores for surrounding parcels alongwith USDA census and sales data to generate an objective valuation ofeach parcel that is expressed in dollars per acre. Other embodimentscontemplate employment of other and/or additional agricultural metricsto arrive at an agricultural valuation that is meaningful to a givenuser's role (e.g., farmer, enterprise farm, banking, underwriting).Further embodiments envision the employment of weighted agriculturalmetrics to generate an agricultural valuation that is meaningful to agiven user's role. The agricultural metrics generated at block 314 alongwith the agricultural valuation(s) for each of the parcels are thenstored in the parcel database 318 along with other parcel data as isdescribed above with reference to FIGS. 1-2.

Turning now to FIG. 4, a flow diagram 400 is presented showingprocessing of remote sensed data for determination of stability zoneswithin a parcel, such as may be performed by the remote sense processor156 of FIG. 1. In one embodiment, the remote sense processor 156processes satellite and/or aerial images to generate data for parcelsthat include a plurality of zones within a parcel that have differinglevels of production stability in view of historical crop production. Inone embodiment, the remote sense processor 156 processes satelliteand/or aerial images to generate data for parcels that include fourzones within a parcel that have differing levels of production stabilityin view of historical crop production. Other numbers of stability zonesare contemplated.

Flow begins at block 402, where the remote sense processor 156 employspublic data to determine a boundary within each of the parcels thatincludes the most common crop type within that parcel and selectsavailable daily (or less frequent) image data for parcels within aprescribed region, as noted above. The image data may be from the publicdatabase 122 or the commercial database 123. As described above, theimages are downloaded, assessed and cleansed, and stored. Flow thenproceeds to block 404.

At block 404, images that have missing data (e.g., covered by clouds)below a prescribed quality threshold are removed and images with missingdata above the prescribed quality threshold are retained. Some of themissing data in the retained images may be estimated by time-processingdata from other time-adjacent images which include that data. Flow thenproceeds to block 406.

At block 406, relevant spectral bands for a given observation arecombined to generate vegetative indices for subparts of the parcelsaccording to well-known techniques. In one embodiment, the techniquedifference includes the normalized vegetation index (NDVI). Anothertechnique includes the enhanced vegetatio index n (EVI). In oneembodiment, red, green, blue, and near-infrared spectral bands arecombined. Flow then proceeds to block 408.

At block 408, the remote sense processor 156 identifies a bestvegetative index image for each of the parcels for each of a number ofprescribed years. In one embodiment, one of the images within a givenyear is selected as the best vegetative index image by summing all ofthe intensities for each valid pixel within the vegetative index imageand selecting the image that has the highest valid pixel sum. Validpixels are those that 1) have an actual intensity value and 2) are notcovered by clouds. As one skilled in the art will appreciate, somepixels within an image will have a “no data” value due to a number ofpossible reasons, such as malfunctions of the imaging system itself.

At block 410, stability zones within each of the parcels are determinedby the remote sense processor 156 from all of the available bestvegetative index images via a stability zone determination algorithmthat will be described in more detail with reference to FIG. 7. Theresult of applying this algorithm is a single composite image where eachpixel in the image represents one of the plurality of stability classes.In one embodiment that contemplates two stability classes, the classesinclude stable and unstable. In another embodiment that contemplatesfour stability classes, the classes include high-stable, medium-stable,low-stable, and unstable. In addition, as one skilled in the art willappreciate, certain pixels within an image may be categorized asundefined, indicating that those pixels are invalid for purposes ofdetermining stability zones.

The concept of agricultural stability zones is well known. For moreinformation, the reader is referred to the following publications:

-   Maestrini, B., Basso, B. Drivers of within-field spatial and    temporal variability of crop yield across the US Midwest. Sci Rep 8,    14833 (2018). https://doi.org/10.1038/s41598-018-32779-3-   Basso, B., Shuai, G., Zhang, J. et al. Yield stability analysis    reveals sources of large-scale nitrogen loss from the US Midwest.    Sci Rep9, 5774 (2019). https://doi.org/10.1038/s41598-019-42271-1-   Maestrini, B., Basso, B. Drivers of within-field spatial and    temporal variability of crop yield across the US Midwest. Sci Rep 8,    14833 (2018). https://doi.org/10.1038/s41598-018-32779-3-   Once the stability zones for each of the parcels are determined,    flow then proceeds to block 412.

At block 412, the stability zones are provided to the parcel database151 for access by the agricultural metrics processor 152.

Now referring to FIG. 5, a flow diagram 500 is presented illustratingprocessing and ranking of agricultural management practices forgeneration of inputs to a crop simulation processor according to thepresent invention. As alluded to above, the management practicesprocessor 155 may access the outputs of the remote sense processor 156to evaluate and rank the outputs against other management practice datathat is received from one or more of the databases 121-124. In turn, themanagement practices processor 155 may augment sparse or incomplete datain order to provide location-specific inferences for key crop managementpractices including, but not limited to planted crop type, specificcultivar or crop variety, planting data, planting density (i.e., rowspacing), dates and amounts of fertilizer application, and irrigationpractices. Flow begins at block 502 where all available managementpractices datasets are selected for each of the parcels. As one skilledin the art will appreciate, management practice data and correspondingdatasets are highly dependent on location and type of managementpractice. For some practices and locations, a trusted dataset may beavailable, but which is incomplete along with a less-trusted, butcomplete dataset. In addition, available datasets may be more or lessgeographically granular ranging from state averages, to county averages,all the way down to data based on 30-meter grid. Finally, somemanagement practices also may rely on a heuristic devised byagronomists. For example, a common rule of thumb for determining howmuch fertilizer a farmer would typically use is based on the expectedamount of yield for a given crop. Flow then proceeds to block 504.

At block 504, a next management practice is selected and flow proceedsto block 506.

At block 506, all of the available datasets for the selected managementpractice are evaluated and ranked according to quality. This ranking isperformed by directives that rank the available datasets according totheir ability to generate inputs to the crop simulation processor 153that will produce outputs that are accurate when compared to fieldtrials and scientific data. Flow then proceeds to block 508.

At block 508, for the selected management practice, the highest rankedmanagement practice dataset is selected for generation of cropsimulation inputs. Flow then proceeds to decision block 510.

At decision block 510, an evaluation is made to determine if there areany remaining management practices whose datasets have not beenassessed, ranked, and selected for generation of crop simulation inputs.The present inventors note that the selected value for one managementpractice may affect the selection of another. For instance, if corn isselected as a crop, then the planting date must be a date which issuitable for planting corn at the given location. A different cropselection would result in a different planting date selection.Accordingly, the management practices processor 155 develops directivesthat take into account dependency ordering of different managementpractices. If there are remaining management practices, then flowproceeds to block 504. If not, then flow proceeds to block 512.

At block 512, the highest ranked management practices datasetcorresponding to each management practice is provided to a simulationinput builder that generates inputs for the crop simulation processor153.

Steps 502-512 are performed for each of the parcels. Accordingly,simulation inputs are generated for each of the parcels that employ thehighest ranked management practices datasets.

Now turning to FIG. 6, a flow diagram 600 is presented detailing amethod for translation of crop simulation outputs into a productivitymetric for an agricultural parcel that ranks the parcel relative to allother parcels within a specified growing region. The method will bedescribed in terms of rotating crops (e.g., corn and soybeans, wheat andbroadleaf crops, etc.) in order to clearly teach aspects of the presentinvention; however, the present inventors note that the method may alsobe modified to apply to crops that aren't rotated (e.g., light feeders),though such a practice is less common and not sustainable. Flow beginsat block 602 where rotatable crop types are selected for each of theparcels using data derived by the remote sense processor 156. Flow thenproceeds to block 604.

At block 604, simulation inputs provided by the management processor 155along with soil types, topography, weather, and location data providedby the databases 121-124 are employed by the crop simulation processor153 to simulate crop yields for individual soil zones within each of theparcels for a prescribed number of prior years, rotating crops everyother year. This step is performed twice. First, a primary crop issimulated in even years and a secondary crop is simulated in odd years.Next, the secondary crop is simulated in even years and the primary cropis simulated in odd years. Thus, yields of the primary crop aresimulated for each year (both odd and even years). The USDA SSURGO soildatabase is employed to divide up the tillable area of the parcels intosoil zones. In one embodiment, the prescribed number of years is 10years. Another embodiment contemplates 16 prior years. Flow thenproceeds to block 606.

At block 606, the agricultural metrics processor 152 generates aweighted average of yields of the primary crop over all usable soilzones within each of the parcels to determine yearly primary crop yieldfor each of the prescribed number of years, where the weight for a givensoil zone comprises the portion of the tillable soil zones occupied bythe given soil zone. Next, an average primary crop yield for theprescribed number of years is determined for each of the parcels bydividing the sum of the yearly primary crop yields by the prescribednumber of years. Thus, the metrics processor 152 generates an averageprimary crop yield for each of the parcels. Flow then proceeds to block608.

At block 608, the average yearly primary crop yield for each of theparcels is normalized against all other average yearly primary cropyields within a prescribed region. In one embodiment, the regioncomprises state level. Another embodiment comprises a growing region(e.g., corn belt, wheat belt). Normalization generates a first numberthat is less than one for each parcel. The first number is a parcel'saverage yearly primary crop yield divided by a prescribed maximum yearlyyield that represents the greatest average primary crop yield in theprescribed region. In one embodiment, the prescribed maximum yearlyyield is slightly higher than the highest average primary crop yield forall of the parcels in the prescribed region. Next the first number ismultiplied by 100 to generate a parcel yield number for each of theparcels that is between 0 and 100. Flow then proceeds to block 612.

At block 612, the metrics processor 152 generates a productivity metric(or, “score”) for each parcel that is equal to its parcel yield number.Accordingly, a given parcel having a productivity score of, say, 90indicates that the given parcel has had primary crop yields over thepast X years that are higher is 90 percent of the prescribed maximumyearly yield. A productivity score of 90 additionally indicates that thegiven parcels has 50% higher yields than a parcel having a productivityscore of 60.

Advantageously, the productivity metric generation method according tothe present invention translates commercial and public data, remotelysensed data, topography data, farm management practices, field trialsdata, and crop simulation data into an agriculturally meaningful metricthat expresses a parcel's historical productivity relative to all otherparcels within a prescribed region so that a user predominatelyinterested in productivity can make decisions more accurately than hasheretofore been provided for by present-day techniques.

Referring now to FIG. 7, a flow diagram 700 is presented detailing amethod for translation of remote sensed data into a stability metric foran agricultural parcel according to the present invention. Herein, thestability metric may be referred to as a stability score as well as afield reliability score. As noted above, a parcel's stability scorecharacterizes the risk associated with production of a primary crop overa range of years. In order to clearly teach aspects of the presentinvention, the flow diagram 700 describes the process executed by theagricultural valuation server 130 to derive a stability score for agiven parcel; however, the present inventors note that the valuationserver 130 derives stability scores for all of the parcels within aprescribed region. Flow begins at block 702 where publicly availabledata is processed by the remote sense processor 156 to determine asubregion of the parcel that includes the most common crop type withinthat parcel. This determination is currently based on the publiclyavailable CropScape and Cropland Data Layer database maintained by theUSDA National Agricultural Statistics Service. The boundarydetermination is made for each year within a range of years. In oneembodiment, the range comprises the previous 10 years. Anotherembodiment contemplates a range of 16 years. Flow then proceeds to block704.

At block 704, available daily (or less frequent) image data for parcelswithin the prescribed region is acquired. As noted above, the image datamay be from the public database 122 or the commercial database 123, andthe images are downloaded, assessed and cleansed, and stored. Flow thenproceeds to block 706.

At block 706, images that have missing data (e.g., covered by clouds)below a prescribed quality threshold are deleted. Images with missingdata above the quality threshold are retained and some of the missingdata may be estimated by time-processing data from other time-adjacentimages which include those pixels. Flow then proceeds to block 708.

At block 708, relevant spectral bands for a given observation arecombined to generate vegetative indices for subparts of the parcels asis described above with reference to FIG. 4. In one embodiment, red,green, blue, and near-infrared spectral bands are combined. Flow thenproceeds to block 710.

At block 710, stability zones within each of the parcels are determinedby the remote sense processor 156 by identifying a best vegetative indeximage for each of the parcels for each of a number of prescribed years.In one embodiment, one of the images within a given year is selected asthe best vegetative index image by summing all of the intensities foreach valid pixel within the vegetative index image and selecting theimage that has the highest valid pixel sum. Valid pixels are thosethat 1) have an actual intensity value and 2) are not covered by clouds.As one skilled in the art will appreciate, some pixels within an imagewill have a “no data” value due to a number of possible reasons, such asmalfunctions of the imaging system itself. At block 710, the sum for ofthe intensities in each of the images is computed. Flow then proceeds toblock 712.

At block 712, a best vegetative index image is selected for each of theparcels for each year within the prescribed number of years. In oneembodiment, the best vegetative index image comprises the image that hasthe highest valid pixel sum. Flow then proceeds to block 714.

At block 714, all of the yearly best vegetative index images for eachparcel are processed as is discussed above with reference to FIG. 4according to a stability zone algorithm to generate a single compositeimage where each valid pixel in the image represents one of a pluralityof stability classes. In an embodiment that contemplates four stabilityclasses, valid pixels are assigned as being high-stable, medium-stable,low-stable, and unstable. Going forward, to clearly teach the presentinvention, this embodiment will be discussed; however, the presentinventors note that different numbers of stability classes (e.g., 2stability classes, 3 stability classes, etc.) are contemplated. Thestability zone algorithm is referred to also as HMLU algorithm. Inoperation the HMLU algorithm assigns to each pixel of the singlecomposite image for a given parcel into one of five labels: high-stable(H), medium-stable (M), low-stable (L), unstable (U), and no-data. Thealgorithm has three prescribed parameters: a low threshold, a highthreshold, and an unstable threshold. The algorithm processes the singlecomposite image as follows:

-   -   “No-data” is assigned if that pixel is missing in any of the        images across all years; otherwise the pixel is considered valid    -   If the pixel is valid:        -   “U” is assigned if the standard deviation of that pixel            across all years is higher than the unstable threshold,            otherwise the pixel is considered stable    -   if the pixel is stable:        -   “H” is assigned if the mean of that pixel across all years            is above the high threshold        -   “L” is assigned if the mean of that pixel across all years            is below the low threshold        -   “M” is assigned if the mean of that pixel across all years            is between the low and high thresholds            Flow then proceeds to block 716.

At block 716, the metrics processor 152 computes a unique parcelstability metric by summing the pixels in the parcel's single compositeimage that are high-stability (H) or medium-stability (M) and dividingthis sum by the sum of all pixels in the single composite images thatare deemed valid by the HMLU algorithm, which yields a number between 0and 1. This number is then multiplied by 100 to produce a stabilityscore for each of the parcels that ranges from 0 to 100. Thus, a parcelwith a low stability score is seen as a higher production risk relativeto another parcel with a high stability score. As one skilled in the artwill appreciate, though a stability score is computed for a given parcelusing data that only corresponds to the given parcel, such a stabilityscore is indeed correlated with land value, thus providing a meanswhereby the stability of the given parcel may be compared to otherparcels within the prescribed region.

Advantageously, the stability metric generation method according to thepresent invention translates commercial and public data along withremotely sensed data into an agriculturally meaningful metric thatexpresses a parcel's historical productivity risk relative to all otherparcels within a prescribed region so that a user predominatelyinterested in productivity risk can make decisions more accurately thanhas heretofore been provided for by present-day techniques.

Now turning to FIG. 8, a flow diagram 800 is presented detailing amethod for translation of crop simulation outputs into a sustainabilitymetric for an agricultural parcel that ranks a given parcel relative toall other parcels within a specified growing region. As one skilled inthe art will appreciate, farming the landscape requires time, energy,and inputs such as nitrogen fertilizer. Complex interactions between theland, the weather, and the way a crop is managed determine the requiredinputs to maximize crop yield and the impact those inputs have on theenvironment. Understanding the overall sustainability of a farm requiresa deep understanding of the land itself, as well as how it has beenmanaged over time. Recognizing that some parcels require greater inputsto maintain yields than are required by other parcels, the method fortranslation of crop simulation outputs into a sustainability metricemploys the crop simulation processor 153 to model the day to day growthof a crop on a particular parcel, together with the nutrients requiredto sustain it as provided by the management practices processor 155.Like the method for translating modeled yield into a productivity metricas described above, the method for generating the sustainability metricmodels not only the yield for a parcel, but also the amount of nitrogenleached into groundwater and the quantity of greenhouse gasses emittedin the production process. These components are generated by the cropsimulation processor 153 and are generated uniquely for each parcel,taking into account local soil and weather conditions along with bestmanagement practices as provided by the management processor 155.

In one embodiment, a sustainability metric (or, “score”) for a parcelcomprises a number of component scores, each corresponding to a specificmeasure of sustainability. In one embodiment, the component scorescomprise a nitrogen leaching score and a greenhouse gas emissions score.Other embodiments contemplate additional component scores such as, butnot limited to, irrigation use scores, energy use scores, soil erosionscores, soil carbon exchange scores, biodiversity scores, and waterquality scores. As will be described in further detail below, componentscores range from 0 to 100 and indicate a quantile into which thatcomponent value falls over a prescribed region (e.g., state, county,growing region). For example, if a parcel's nitrogen leaching score is75, it means that the amount of nitrogen leached out of that parcel asmodeled by the crop simulation processor 153 is better thanthree-quarters of the parcels in the prescribed region.

In one embodiment, the nitrogen leaching score generated by the cropsimulation processor 153 expresses the annual amount of nitrogen thatleaves a parcel and enters groundwater. The greenhouse gas emissionsscore expresses the annual amount of greenhouse gases that are releasedfrom a parcel, where the annual amount of greenhouse gases releasedcomprises the sum of four subcomponents: carbon dioxide flux from thesoil, nitrous oxide flux from the soil, carbon dioxide from tractor fueluse, and carbon dioxide from production of nitrogen fertilizer. It isbeyond the scope of the present application to provide an in-depthdiscussion of how these component scores are calculated; however, it issufficient to note that these component scores are calculated accordingto Intergovernmental Panel on Climate Change (IPCC) Tier 1 methods, andare processed by the metrics processor 152 as is described below toprovide an overall sustainability metric that expresses how a givenparcel is sustainable relative to all other parcels within theprescribed region.

The method of FIG. 8 will now be described in terms of rotating crops(e.g., corn and soybeans, wheat and broadleaf crops, etc.) in order toclearly teach aspects of the present invention; however, the presentinventors note that the method may also be modified to apply to cropsthat aren't rotated (e.g., light feeders), though such a practice isless common and not sustainable. Flow begins at block 802 whererotatable crop types are selected for each of the parcels over aprescribed number of years using data derived by the remote senseprocessor 156. In one embodiment, the prescribed number of yearscomprises 16 years, though other year ranges are contemplated. Flow thenproceeds to block 804.

At block 804, simulation inputs provided by the management processor 155along with soil types, topography, weather, and location data providedby the databases 121-124 are employed by the crop simulation processor153 to simulate crop yields for individual soil zones within each of theparcels for the prescribed number of prior years, rotating crops everyother year. This step is performed twice. First, at block 804, a primarycrop is simulated in even years and a secondary crop is simulated in oddyears. Next, at block 806, the secondary crop is simulated in even yearsand the primary crop is simulated in odd years. Thus, yields andsustainability components of the primary crop are simulated andgenerated for each year (both odd and even years). Flow then proceeds toblock 808.

At block 808, yearly sustainability components as described above asprovided by the crop simulation processor 153 are extracted by theagricultural metrics processor 152. Flow then proceeds to block 810.

At block 810, the metrics processor 152 calculates an average yearlycomponent value for each of the sustainability components provided bythe simulation processor 153. Flow then proceeds to block 812.

At block 812, the metrics processor 152 processes the average yearlycomponent values for all of the parcels by assigning the average yearlycomponent values to quantiles ranging from 0 to 100 relative to all ofthe parcels within the prescribed region, thus yielding a quantile scorefor each sustainability component for each of the parcels. Flow thenproceeds to block 814.

At block 814, the metrics processor 152 calculates a mean of thecomponent quantile scores for each of the parcels, thus yielding anaverage parcel sustainability component score. Flow then proceeds toblock 816.

At block 816, the average parcel sustainability component scores areassigned to quantiles ranging from 0 to 100 relative to all of theparcels within the prescribed region. The quantile into which a giventhe average parcel sustainability component score falls is the parcel'ssustainability score.

Advantageously, the sustainability metric generation method according tothe present invention translates commercial and public data, remotelysensed data, topography data, farm management practices, field trialsdata, and crop simulation data into an agriculturally meaningful metricthat expresses a parcel's sustainability attribute relative to all otherparcels within a prescribed region so that a user predominatelyinterested in sustainable farming (e.g., reducing carbon dioxideemissions, organic farming methods, etc.) can make decisions moreaccurately than has heretofore been provided for by present-daytechniques.

Referring now to FIG. 9, a flow diagram 900 is presented detailing amethod for translation of agricultural metrics and comparable salesprices into an agriculturally meaningful valuation for an agriculturalparcel relative to all other parcels within a specified growing region.As noted above, the valuation assigned to a parcel employs one or moreof the agricultural metrics (e.g., productivity scores, stabilityscores, and sustainability scores) as a function of a user's role assupplemented by comparable sales to express an agricultural value indollars per acre as opposed to just a value that is based on comparableparcels. Advantageously, the user is exposed to a valuation of a parcelbased upon the parcel's agricultural potential, which is a substantialimprovement over that which has heretofore been provided.

In one embodiment, as will be described in further detail below, theproductivity score for a given parcel is employed in conjunction withproductivity scores for surrounding parcels along with USDA census andsales data to generate the agricultural valuation. Other embodimentscontemplate employment of other and/or additional agricultural metricsto arrive at an agricultural valuation that is meaningful to a givenuser's role (e.g., farmer, enterprise farm, banking, underwriting).Further embodiments envision the employment of weighted agriculturalmetrics to generate an agricultural valuation that is meaningful to agiven user's role.

Flow begins at bock 902, where the agricultural valuation server 130retrieves the mean sales value for all parcels within a prescribedregion, where the prescribed region may comprise county, state, orgrowing region. The mean sales value, MEAN SALES VALUE is retrieved fromthe public database 122, and more specifically the USDA Census ofAgriculture provided by the National Agricultural Statistics Service.Flow then proceeds to block 904.

At block 904, the valuation processor 154 calculates a standarddeviation of the mean sales value, STDEV(MEAN SALES VALUE) by dividingMEAN SALES VALUE by 100 and multiplying the result by the USDACoefficient of Variation for the prescribed region. Flow then proceedsto block 906.

At block 906, the valuation processor 154 retrieves the productivitymetrics for all parcels within the prescribed region from the parceldatabase 151. Next the valuation processor 154 calculates a mean of allthe productivity scores within the region, MEAN(PRODUCTIVITY SCORES)along with a standard deviation of the productivity scoresSTDDEV(PRODUCTIVITY SCORES). Flow then proceeds to block 908.

At block 908, the valuation processor 154 calculates a weightedproductivity metric WP for each parcel in the prescribed region, whereWP represents a weighted average productivity score for all tillableacres within the parcel. Recall that different soil zones as defined bythe USDA SSURGO soil database exhibit differing productivity scores,reflecting each particular soil zone's ability to produce. WP for agiven soil zone, WP(ZONE) is calculated by multiplying each soil zone'sproductivity by the acreage of that soil zone. Next, WP for the parcelis calculated by summing all WP(ZONE) values for all zones in the parceland dividing by the total tillable acreage of the parcel. Flow thenproceeds to block 910.

At block 910 an objective valuation AG VALUATION for each parcel iscalculated as follows:

AG VALUATION=MEAN SALES VALUE+[STDDEV(SALES VALUE)/STDDEV(PRODUCTIVITYSCORES)]*[WP−MEAN(PRODUCTIVITY SCORES)].

In one embodiment, the productivity scores, stability scores, andsustainability scores for parcels are employed as supplemented bycommercial sales prices to translate these metrics into agriculturalvaluations that may be more useful to a particular type of user, whereweights are assigned to each of the scores in accordance with the levelof insight that a that is required by the particular type of user. Forexample, a single farmer may be focused solely on productivity of aparcel and would hence employ AG VALUATION as discussed with referenceto block 910. Alternatively, a lender may be equally interested equallyin production and production risk (i.e., stability) and would beinfluenced by an agricultural valuation that equally weights bothproductivity scores and stability scores for parcels. An enterprisefarming corporation may be focused exclusively on carbon offsets and maybe influenced by an agricultural valuation that weights sustainabilityover production. Accordingly, the present invention contemplates theabove embodiments as provided for by the valuation method describedabove.

Advantageously, the agricultural valuation provided for by the presentinvention is configurable to generate a values for parcels thatconsiders what is most important to a particular user, which is asignificant improvement over present-day valuation methods thatexclusively consider commercial sales prices.

Turning now to FIG. 10, a block diagram is presented illustrating anagricultural valuation server 1000 according to the present invention,such as the valuation server 130 of FIG. 1. The valuation server 1000may include one or more central processing units (CPU) 1001 that arecoupled to memory 1006 having both transitory and non-transitory memorycomponents therein. The CPU 1001 is also coupled to a communicationscircuit 1002 that couples the valuation server 1000 to the internetcloud 110 via one or more wired and/or wireless links 1003. The links1003 may include, but are not limited to, Ethernet, cable, fiber optic,and digital subscriber line (DSL). As part of the network path to andthrough the cloud 110, providers of internet connectivity (e.g., ISPs)may employ wireless technologies from point to point as well.

The valuation server 1000 may also comprise input/output circuits 1004that include, but are not limited to, data entry and display devices(e.g., keyboards, monitors, touchpads, etc.). The memory 1006 may becoupled to a parcel database 1005 and to the databases 121-124 describedwith reference to FIG. 1 above. Though the valuation server 1000 isshown directly coupled to databases 121-124 and 1005, the presentinventors note that interfaces to these data sources may exclusively bethrough the communications circuit 1002 or may be through a combinationof direct interface and through the communications circuit 1002,according to the source of data.

The memory 1006 may include an operating system 1007 such as, but notlimited to, Microsoft Windows, Mac OS, Unix, and Linux, where theoperating system 1007 is configured to manage execution by the CPU 1001of program instructions that are components of one or more applicationprograms. In one embodiment, a single application program comprises aplurality of code segments 1008-1016 resident in the memory 206 andwhich are identified as a configuration code segment CONFIG 1008, aclient communications code segment CLIENT COMM 1009, a presentationprocessor code segment PRESENTATION PROC 1010, a web services codesegment WEB SERV 1011, an agricultural metrics processor code segment AGMETRICS PROC 1012, a crop simulation processor code segment CROP SIMPROC 1013, a valuation processor code segment VALUATION PROC 1014, amanagement practices processor code segment MGMT PROC 1015, and a remotesense processor code segment REM SENSE PROC 1016.

Operationally, the valuation server 1000 may execute one or more of thecode segments 1008-1016 under control of the OS 1007 as required toenable the valuation server 1000 to ingest new data from external datasources 121-124, to employ data from the sources 121-124 in cropsimulations that translate the data into meaningful agricultural metricsand corresponding valuations for approximately 20 million agriculturalparcels, and to store these metrics and valuations in the parceldatabase 1005 in a manner that can be rapidly and easily searched andaccessed by users that communicate with the valuation server 1000 overthe communications circuit 1002 via client applications 104-106executing on their respective client devices 101-103. The valuationserver 1000 may further be configured to execute one or more of the codesegments 1008-1016 under control of the OS 1007 as required to enablethe valuation server 1000 to format and present search results andcorresponding parcel data to the client applications 104-106 executingon their respective client devices 101-103 and to receive communicationstherefrom that users specify to narrow search results, to perform newsearches altogether, and to specify the relative importance of one ormore agricultural metrics relative to other agricultural metrics inorder to calculate agricultural valuations of parcels according torelative importance of the agricultural metrics relative to otheragricultural metrics.

CONFIG 1008 may be executed to place the server 1000 into an operationalor maintenance mode, where the maintenance mode may be entered to allowfor ingestion of new data from the data sources 121-124 via automated ormanual means. CLIENT COMM 1009 may be executed to perfect reliabletransfer of information between the valuation server 1000 and clientapplications 104-106 executing on respective client devices 101-103.PRESENTATION PROC 1010 may be executed to perform searches of the parceldatabase 1005, to provide search results, and to interact with clientapplications 104-106 executing on respective client devices 101-103 asis described above with reference to FIGS. 1-3. WEB SERV 1011 may beexecuted to provide for formatting of information provided byPRESENTATION PROC 1010 for transmission to the client applications104-106 and for formatting of information that is provided toPRESENTATION PROC 1010 which has been received from the clientapplications 104-106.

AG METRICS PROC 1012 may be executed to perform any of the functions andoperations described above with reference to the agricultural metricsprocessor 152 of FIG. 1. CROP SIM PROC 1013 may be executed to performany of the functions and operations described above with reference tothe crop simulation processor 153 of FIG. 1. VALUATION PROC 1014 may beexecuted to perform any of the functions and operations described abovewith reference to the agricultural valuation processor 154 of FIG. 1.MGMT PROC 1015 may be executed to perform any of the functions andoperations described above with reference to the management practicesprocessor 155 of FIG. 1. And REM SENSE PROC 1016 may be executed toperform any of the functions and operations described above withreference to the remote sense processor 156 of FIG. 1.

Now referring to FIG. 11, a block diagram is presented depicting aclient device 1100 according to the present invention, such as theclient devices 101-103 discussed above with reference to FIG. 1. Theclient device 1100 may include one or more central processing units(CPU) 1101 that are coupled to memory 1105 having both transitory andnon-transitory memory components therein. The CPU 1101 is also coupledto a communications circuit 1102 that couples the client device 1100 tointernet cloud 110 via one or more wired and/or wireless links 1103. Thelinks 1103 may include, but are not limited to, Ethernet, cable, fiberoptic, and digital subscriber line (DSL).

The client device 1100 may also comprise input/output circuits 1104 thatinclude, but are not limited to, data entry and display devices (e.g.,keyboards, monitors, touchpads, etc.).

The memory 1005 may include an operating system 1106 such as, but notlimited to, Microsoft Windows, Mac OS, Unix, Linux, iOS, and Android OS,where the operating system 1106 is configured to manage execution by theCPU 1101 of program instructions that are components of a valuationclient application program 1107. In one embodiment, the valuation clientapplication program 1107 comprises a server communications code segmentSERVER COMM 1108 and an I/O interface code segment I/O INTERFACE 1109.

When executing on the client device 1100, the valuation client 1107provides for display of information provided by the valuation server130, 1000 on the input/output circuits 1104 that help a user makedecisions regarding which parameters to specify in order to performsearches of the parcel database 151, 1005. The SERVER COMM 1108 segmentmay execute to receive this information and the I/O INTERFACE segment1109 may execute to transmit this information to the input/outputcircuit 1104. Likewise, the valuation client 1107 provides for input ofsearch parameters provided by the user via the input/output circuit fortransmission to the valuation server 130, 1000 that direct the valuationserver 130, 1000 to refine an ongoing search in order to narrow down anumber of parcels that satisfy the search parameters, to specifyparameters that direct the valuation server 130, 1000 to perform newsearches altogether, and to specify the relative importance of one ormore agricultural metrics relative to other agricultural metrics thatdirect valuation server 130, 1000 to calculate agricultural valuationsof parcels according to the relative importance of the agriculturalmetrics relative to other agricultural metrics. The SERVER COMM 1108segment may execute to transmit this information and the I/O INTERFACEsegment 1109 may execute to receive this information to the input/outputcircuit 1104.

The functions and operations described above with reference to thevaluation server 130, 1000 according to the present invention result ina significant improvement in this field of technology by providing asuperior technique for translating massive amounts of agricultural datafor millions of parcels into agriculturally meaningful metrics andcorresponding agricultural valuations and that aggregate the metrics andvaluation along with public and commercial sales data for these parcelsinto detailed parcel reports that are displayed on client devices101-103, 1100. In one embodiment, data corresponding to the detailedparcel reports are stored in the parcel database 151 for allagricultural parcel in the United States, approximately 20 millionparcels. The parcel report is the culmination all of the functions andoperations described above and includes a combination of data frompublic datasets, commercial datasets, management practices inference,remote sensing inference, crop simulation results, and final metric andagricultural algorithmic results. In one embodiment, to enable a searchinterface for users, the valuation server 130, 1000 indexes key piecesof the data within the parcel database 151 into a full-text searchengine. This allows users to filter through millions of parcels toretrieve those which fit their particular search criteria withsub-second search response time. Exemplary client device displays willnow be presented with reference to FIGS. 12-18 that show how exemplarydata is presented to a user, how exemplary search parameters are inputby the user for transmission to the valuation server 130, 1000, and howinformation is displayed to the user via exemplary detailed parcelreports.

Now turning to FIG. 12, a diagram is presented featuring an exemplaryadvanced search display 1200 according to the present invention, such asmight be presented by the client device 1100 of FIG. 11. The displayincludes an entry field, wherein a user may enter growing region, state,county, zip code, Public Land Survey System (PLSS), keywords, parcelowner name, historical land use (e.g., crop type), land type (e.g.,farm, dairy, ranch, forest, etc.), parcel acreage, tillable area, andagricultural metrics and agricultural valuations generated by thevaluation server 130. In the exemplary embodiment shown, sliders arepresented to adjust both a lower and upper bound for the valuation,productivity score, stability score, total acreage, tillable acreage,and sustainability score. Check boxes are provided for selection of landtype and crop type (i.e., historical land user.

FIG. 13 is a diagram showing an exemplary advanced search interactivedisplay 1300 according to the present invention, such as might bepresented by the client device of FIG. 11. This display 1300 showsadditional display items transmitted by the server 130 when a user hasentered “IOWA” in the search field, that further assist the user inspecifying the remainder of the search field. Items include Iowa City,Iowa; Iowa Park, Tex.; Iowa, La.; etc.

FIG. 14 is a diagram illustrating an exemplary advanced search resultsdisplay 1400 according to the present invention such as might bepresented by the client device of FIG. 11. Once a user has specifiedsearch parameters via interactions with the displays 1200-1300 of FIGS.12 and 13, the server 130 then transmits a list of parcels to the clientdevice 1100 that satisfy the search parameters. In the results display1400 shown, the results are displayed on a drillable map as small dots,and which are further listed on the right side of the display, wherebythe user may scroll and select a detailed parcel report.

FIG. 15 is a diagram detailing an exemplary parcel report with valuationdetails display 1500 according to the present invention, such as mightbe presented by the client device of FIG. 11. In this display 1500, theuser has selected a parcel identified as “S30 79N 6W, Johnson, Iowa.”The display 1500 also shows helpful orientation information for the usersuch as total acres, tillable percentage of total acres, previous croptype, and owner name. The display 1500 further shows the agriculturalvaluation generated by the valuation processor 154 as discussed abovealong with the parcel's productivity, field reliability (i.e.,stability), and sustainability scores that are generated by theagricultural metrics processor 152. In addition, a historical yield ofprimary and secondary crops is shown. This display 1500 also featuresdetails of the agricultural valuation generated by the valuationprocessor 154 as a parcel valuation, valuation per acre, and valuationper tillable acre. In addition to contributing details (e.g., propertytaxes and assessment), the display 1500 shows a graphic of the parcel'svaluation relative to all other parcels in a prescribed region, namelywithin the county.

FIG. 16 is a diagram detailing an exemplary parcel report withproductivity metric details display 1600 according to the presentinvention, such as might be presented by the client device of FIG. 11.This display 1600 is substantially similar to the display 1500 of FIG.15, except that the user has highlighted the productivity score todisplay a graphic of the parcel's productivity score relative to allother parcels in the prescribed region, namely within the county.

FIG. 17 is a diagram detailing an exemplary parcel report with stabilitymetric details display 1700 according to the present invention, such asmight be presented by the client device of FIG. 11. This display 1700 issubstantially similar to the display 1500 of FIG. 15, except that theuser has highlighted the field reliability score to display a graphic ofthe parcel's stability score relative to all other parcels in theprescribed region, namely within the county. In addition to thisgraphic, a color-coded map of the parcel is shown that depicts thevarious stability zones for the parcel as determined by the agriculturalmetrics processor 154.

Finally, FIG. 18 is a diagram detailing an exemplary parcel report withsustainability metric details display 1800 according to the presentinvention, such as might be presented by the client device of FIG. 11.This display 1800 is substantially similar to the display 1500 of FIG.15, except that the user has highlighted the sustainability score todisplay a graphic of the parcel's sustainability score relative to allother parcels in the prescribed region, namely within the county. Inaddition to this graphic, details are displayed regarding the relativecontribution of each of the sustainability components (e.g., nitrogenleaching score and greenhouse gas emissions score) that are employed togenerate the overall sustainability score for the parcel.

Portions of the present invention and corresponding detailed descriptionare presented in terms of software or algorithms, and symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the ones by which those ofordinary skill in the art effectively convey the substance of their workto others of ordinary skill in the art. An algorithm, as the term isused here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, a microprocessor, a central processingunit, or similar electronic computing device, that manipulates andtransforms data represented as physical, electronic quantities withinthe computer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Note also that the software implemented aspects of the invention aretypically encoded on some form of program storage medium or implementedover some type of transmission medium. The program storage medium may beelectronic (e.g., read only memory, flash read only memory, electricallyprogrammable read only memory), random access memory magnetic (e.g., afloppy disk or a hard drive) or optical (e.g., a compact disk read onlymemory, or “CD ROM”), and may be read only or random access. Similarly,the transmission medium may be metal traces, twisted wire pairs, coaxialcable, optical fiber, or some other suitable transmission medium knownto the art. The invention is not limited by these aspects of any givenimplementation.

The particular embodiments disclosed above are illustrative only, andthose skilled in the art will appreciate that they can readily use thedisclosed conception and specific embodiments as a basis for designingor modifying other structures for carrying out the same purposes of thepresent invention, and that various changes, substitutions andalterations can be made herein without departing from the scope of theinvention as set forth by the appended claims. For example,components/elements of the systems and/or apparatuses may be integratedor separated. In addition, the operation of the systems and apparatusesdisclosed herein may be performed by more, fewer, or other componentsand the methods described may include more, fewer, or other steps.Additionally, unless otherwise specified steps may be performed in anysuitable order.

Although specific advantages have been enumerated above, variousembodiments may include some, none, or all of the enumerated advantages.

What is claimed is:
 1. A method for agricultural land parcel valuation,the method comprising: accessing data corresponding to each of aplurality of parcels within a prescribed region, the data comprisingcorresponding management practices, corresponding historical weatherconditions, corresponding locations and topography, corresponding remotesense images, corresponding soil types, and corresponding crop types;assessing and ranking the corresponding management practices for theeach of the plurality of parcels; generating simulation inputs for theeach of the plurality of parcels, wherein the simulation inputs comprisehighest ranked corresponding management practices, the correspondinghistorical weather conditions, the corresponding locations andtopography, the corresponding soil types, and the corresponding croptypes; simulating crop growth for the each of the plurality of parcelsover a prescribed number of previous years, wherein said simulatingemploys the simulation inputs provided by said generating; and employingselected outputs from said simulating to calculate agricultural metricsand a valuation corresponding to the each of the plurality of parcels,wherein the agricultural metrics and the valuation for the each of theplurality of parcels are expressed relative to all of the plurality ofparcels within the prescribed region, the agricultural metricscomprising a productivity metric that is calculated as a function of aweighted average of yearly primary crop yield simulation outputs for theeach of the plurality of parcels, wherein weights for the weightedaverage comprise fractions of tillable acreage for each of a pluralityof soil type zones within the each of the plurality of parcels.
 2. Themethod as recited in claim 1, wherein the prescribed region comprises acounty.
 3. The method as recited in claim 1, wherein the prescribednumber of previous years comprises 16 years.
 4. The method as recited inclaim 1, wherein the agricultural metrics for the each of the pluralityof parcels further comprise a sustainability metric.
 5. The method asrecited in claim 4, wherein the sustainability metric comprises afunction of an average of yearly sustainability component values, andwherein the yearly sustainability component values comprise yearlynitrogen leeching scores and yearly greenhouse gas emissions scores, andwherein the yearly nitrogen leeching scores and the yearly greenhousegas emissions scores are calculated based upon outputs of saidsimulating crop growth for a corresponding year.
 6. The method asrecited in claim 5, wherein the valuation for the each of the pluralityof parcels comprises a function of the productivity metric,corresponding productivity metrics for all of the plurality of parcels,and corresponding sales values for the all of the plurality of parcels.7. The method as recited in claim 6, wherein the agricultural metricsfor the each of the plurality of parcels further comprise a stabilitymetric that is calculated as combining best yearly vegetative indeximages according to the HMLU algorithm into a single HMLU image andcalculating a fraction of the single HMLU image that compriseshigh-stability and medium-stability pixels.
 8. A computer-readablestorage medium storing program instructions that, when executed by acomputer, cause the computer to perform a method for agricultural landparcel valuation, the method comprising: accessing data corresponding toeach of a plurality of parcels within a prescribed region, the datacomprising corresponding management practices, corresponding historicalweather conditions, corresponding locations and topography,corresponding remote sense images, corresponding soil types, andcorresponding crop types; assessing and ranking the correspondingmanagement practices for the each of the plurality of parcels;generating simulation inputs for the each of the plurality of parcels,wherein the simulation inputs comprise highest ranked correspondingmanagement practices, the corresponding historical weather conditions,the corresponding locations and topography, the corresponding soiltypes, and the corresponding crop types; simulating crop growth for theeach of the plurality of parcels over a prescribed number of previousyears, wherein said simulating employs the simulation inputs provided bysaid generating; and employing selected outputs from said simulating tocalculate agricultural metrics and a valuation corresponding to the eachof the plurality of parcels, wherein the agricultural metrics and thevaluation for the each of the plurality of parcels are expressedrelative to all of the plurality of parcels within the prescribedregion, the agricultural metrics comprising a productivity metric thatis calculated as a function of a weighted average of yearly primary cropyield simulation outputs for the each of the plurality of parcels,wherein weights for the weighted average comprise fractions of tillableacreage for each of a plurality of soil type zones within the each ofthe plurality of parcels.
 9. The computer-readable storage medium asrecited in claim 8, wherein the prescribed region comprises a county.10. The computer-readable storage medium as recited in claim 8, whereinthe prescribed number of previous years comprises 16 years.
 11. Thecomputer-readable storage medium as recited in claim 8, wherein theagricultural metrics for the each of the plurality of parcels furthercomprise a sustainability metric.
 12. The computer-readable storagemedium as recited in claim 11, wherein the sustainability metriccomprises a function of an average of yearly sustainability componentvalues, and wherein the yearly sustainability component values compriseyearly nitrogen leeching scores and yearly greenhouse gas emissionsscores, and wherein the yearly nitrogen leeching scores and the yearlygreenhouse gas emissions scores are calculated based upon outputs ofsaid simulating crop growth for a corresponding year.
 13. Thecomputer-readable storage medium as recited in claim 12, wherein thevaluation for the each of the plurality of parcels comprises a functionof the productivity metric, corresponding productivity metrics for allof the plurality of parcels, and corresponding sales values for the allof the plurality of parcels.
 14. The computer-readable storage medium asrecited in claim 13, wherein the agricultural metrics for the each ofthe plurality of parcels further comprise a stability metric that iscalculated as combining best yearly vegetative index images according tothe HMLU algorithm into a single HMLU image and calculating a fractionof the single HMLU image that comprises high-stability andmedium-stability pixels.
 15. A system for agricultural land parcelvaluation, the system comprising: an agricultural valuation server,configured to access data corresponding to each of a plurality ofparcels within a prescribed region, said data comprising correspondingmanagement practices, corresponding historical weather conditions,corresponding locations and topography, corresponding remote senseimages, corresponding soil types, and corresponding crop types, saidagricultural valuation server comprising: a management practicesprocessor, configured to assess and rank said corresponding managementpractices for said each of said plurality of parcels, and configured togenerate simulation inputs for said each of said plurality of parcels,wherein said simulation inputs comprise highest ranked correspondingmanagement practices, said corresponding historical weather conditions,said corresponding locations and topography, said corresponding soiltypes, and said corresponding crop types; a crop simulation processor,coupled to said management practices processor, configured employ saidsimulation inputs to simulate crop growth for said each of saidplurality of parcels over a prescribed number of previous years; anagricultural metrics processor, configured to employ selected outputsfrom said simulating to calculate agricultural metrics corresponding tosaid each of said plurality of parcels, wherein said agriculturalmetrics are expressed relative to all of said plurality of parcelswithin said prescribed region, said agricultural metrics comprising aproductivity metric that is calculated as a function of a weightedaverage of yearly primary crop yield simulation outputs for the each ofthe plurality of parcels, wherein weights for the weighted averagecomprise fractions of tillable acreage for each of a plurality of soiltype zones within the each of the plurality of parcels; and a valuationprocessor, configured to employ selected outputs from said simulating tocalculate a valuation corresponding to said each of said plurality ofparcels, wherein said valuation is expressed relative to all of saidplurality of parcels within said prescribed region.
 16. The system asrecited in claim 15, wherein said prescribed region comprises a county.17. The system as recited in claim 15, wherein said agricultural metricsfor a given parcel further comprise a sustainability metric.
 18. Thesystem as recited in claim 17, wherein said sustainability metriccomprises a function of an average of yearly sustainability componentvalues, and wherein said yearly sustainability component values compriseyearly nitrogen leeching scores and yearly greenhouse gas emissionsscores, and wherein said yearly nitrogen leeching scores and said yearlygreenhouse gas emissions scores are calculated based upon outputs ofsaid crop simulation processor for a corresponding year.
 19. The systemas recited in claim 18, wherein said valuation for said each of saidplurality of parcels comprises a function of said productivity metric,corresponding productivity metrics for all of said plurality of parcels,and corresponding sales values for said all of said plurality ofparcels.
 20. The system as recited in claim 17, wherein saidagricultural metrics for said each of said plurality of parcels furthercomprise a stability metric that is calculated as combining best yearlyvegetative index images according to the HMLU algorithm into a singleHMLU image and calculating a fraction of said single HMLU image thatcomprises high-stability and medium-stability pixels.